Publications

Portone, T., Khalil, M., & Neal, K.D. (2025). Trustworthy and Scalable Data-Driven Closure Models [Presentation]. 10.2172/2586067

Schafer, T., Brightenburg, W., Neal, K.D., Portone, T., & Khalil, M. (2024). MACHINE LEARNING DATA-DRIVEN CLOSURE MODELS [Presentation]. 10.2172/2585756

Neal, K.D., Karve, P.M., Mahadevan, S., Pack, A.R., & Mullins, J.G. (2024). A Demonstration of the Quantitative Approach for Model Credibility on an Electromagnetic Application [Conference Presentation]. 10.2172/2585120

Taylor, S.N., Neal, K.D., & Acquesta, E.C.S. (2024). Classification Using Support Vector Machines with Uncertainty Quantification [Conference Presentation]. 10.2172/2584949

Neal, K.D., Khalil, M., & Portone, T. (2024). Trustworthy and Scalable Data-Driven Closure Models [Conference Presentation]. 10.2172/2540418

Khalil, M., Portone, T., & Neal, K.D. (2024). Bayesian Model Selection for Capturing Closure Models in Nonlinear Dynamics [Conference Presentation]. 10.2172/2540408

Neal, K.D., Mullins, J.G., Mahadevan, S., & Stover, O. (2023). An Extension to the Predictive Capability Maturity Model to Assess Model Credibility Using Dempster-Shafer Theory [Conference Presentation]. 10.2172/2431228

Neal, K.D., Acquesta, E.C.S., & Rushdi, A. (2023). Quantifying Data Uncertainty in Scientific Machine Learning [Conference Presentation]. 10.2172/2431202

Neal, K.D., Subramanian, A., Mahadevan, S., Mullins, J.G., & Schroeder, B.B. (2022). Model Form Error Correction in Computational Simulations [Conference Presentation]. 10.2172/2005644

Neal, K.D., Foulk, J.W., Lance, B., Rider, W.J., Barone, M.F., & Balakrishnan, U. (2022). Data Credibility in Scientific Machine Learning [Conference Presentation]. 10.2172/2003318

Rushdi, A., Foulk, J.W., Huerta, J.G., Neal, K.D., Dytzel, I., & Rider, W.J. (2022). Quantifying Uncertainty in Machine Learning Models for Time Series Classification [Conference Presentation]. 10.2172/2002430

Roberts, S.A., Donohoe, B.D., Martinez, C., Krygier, M., Hernandez-Sanchez, B.A., Foster, C.W., Collins, L.N., Greene, B., Noble, D.R., Norris, C., Potter, K.M., Roberts, C., Neal, K.D., Bernard, S.R., Schroeder, B.B., Trembacki, B., Labonte, T., Sharma, K., Ganter, T., … Smith, M.D. (2021). Credible, Automated Meshing of Images (CAMI) [Presentation]. https://www.osti.gov/biblio/1900115

Neal, K.D., Schroeder, B.B., Mullins, J.G., & Carnes, B.R. (2021). Extracting Low-Dimensional Features From Field Data for Calibration [Conference Presentation]. 10.2172/1888654

Neal, K.D., Schroeder, B.B., Mullins, J.G., Subramanian, A., & Mahadevan, S. (2021). Robust importance sampling for bayesian model calibration with spatiotemporal data. International Journal for Uncertainty Quantification, 11(4), pp. 59-80. 10.1615/int.j.uncertaintyquantification.2021033499

Guerin, L.C.M., Schroeder, B.B., Mullins, J.G., Neal, K.D., & Roberts, S.A. (2020). Model Calibration in Latent Response Space Using Principal Component Analysis [Conference Poster]. https://www.osti.gov/biblio/1783639

Neal, K.D., Schroeder, B.B., Mullins, J.G., Mahadevan, S., & Subramanian, A. (2019). Bayesian Calibration of the Thermal Battery [Presentation]. https://www.osti.gov/biblio/1645837

Neal, K.D., Schroeder, B.B., Mullins, J.G., & Mahadevan, S. (2018). Uncertainty Quantification in Thermal Battery Performance Using a Roll-up Methodology [Conference Poster]. https://www.osti.gov/biblio/1592651

Neal, K.D., Mahadevan, S., Mullins, J.G., & Schroeder, B.B. (2018). Uncertainty Quantification in Thermal Battery Performance Using a Roll-up Methodology [Conference Poster]. https://www.osti.gov/biblio/1592295

Neal, K.D., Mullins, J.G., & Schroeder, B.B. (2018). Bayesian Calibration Validation and Rollup on Thermal Battery [Presentation]. https://www.osti.gov/biblio/1582154

Neal, K.D. (2017). Quantifying and Reducing the Uncertainty in Thermal Battery Predicted Performance [Presentation]. https://www.osti.gov/biblio/1462514